1. Field of the Invention
The invention relates to a method for deriving reliable information on respiratory activity from the heart beat data, wherein a sub-method, which is based on a heart rate variability, is used, wherein the respiratory frequency is determined from a pattern of rhytmic changes in heart beat data. The present invention relates generally to the monitoring of physiological parameters, in particular to those that are aimed to describe autonomic nervous system function. More specifically, the invention relates to a system for deriving respiratory information on the basis of ECG-signal alone and without being forced to set up an external device for respiratory measurement.
2. Description of the Prior Art
Heart period is among the most commonly used parameters in physiological monitoring. The wide use of heart period is related, on the one hand, to the availability of electrocardiograph (ECG) acquisition devices for noninvasive monitoring and, on the other hand, to its central role in the autonomic nervous system function and sensitivity to several physiological states and conditions in both clinical and non-clinical settings. Heart period (or, its reciprocal heart rate) forms a basis for different types of analyses and may be defined as the series of intervals between consecutive QRS-waveforms in the ECG-signal (see FIG. 2).
The fact that heart period is a complex product of several physiological mechanisms poses a challenge to the use of heart period in applied contexts. This is especially the case within ambulatory measurement, that is, measurement that is performed within natural, free-living conditions, outside of controlled laboratory environment and protocols. However, the multidetermined nature of the heart period may also bring forth a derivation of additional physiological measures from the heart period signal by means of decomposing the heart period into separate components that have a physiological interpretation.
Heart rate variability (HRV) is a general term used for describing periodic changes in heart period [1]. The so-called high frequency (HF)-component of HRV has an approximate frequency range of 0.15-0.50 Hz and is generally accepted as being reflective of parasympathetic nervous system activity. The rhythmic changes in the heart period consist of accelerative and decelerative changes in heart period that are mediated by consecutive withdrawal and re-gain of parasympathetic inhibitory drive on the sinoatrial node of the heart. The inspiration and expiration phases of a breathing cycle are associated with accelerative and decelerative changes in the heart period. Accordingly, during steady conditions, a pattern of rhythmic changes may be observed in the breathing frequency of the heart period (see FIG. 3a). It has been shown that the amplitude of these rhythmic changes, the respiratory sinus arrhythmia (RSA), reflects a level of tonic parasympathetic activity and may be therefore regarded as a noninvasive index of parasympathetic outflow to the heart.
The association between the breathing frequency and heart period brings forth the description of respiratory period on the basis of rhythmic changes in heart period. Although the respiratory component of HRV is clear during relatively steady conditions (e.g., metronome paced breathing in the laboratory, as described in FIG. 3b), the identification and accuracy of the RSA frequency diminishes considerably whenever the heart period signal obtained during ambulatory monitoring includes nonstationary changes that occur in either the breathing cycle or HRV. FIGS. 4a -c demonstrate the complexity and nonstationarities of the heart period signal during non-controlled, ambulatory measurement.
It is generally acknowledged that nonstationarities are rather the rule than exception in cardiovascular dynamics. Although this is clearly the case for ambulatory monitoring, it is also of note that it often difficult to obtain full control even in the laboratory environment. The relationship between the RSA frequency and respiratory period may be inflated by several known and unknown sources of naturally occurring nonstationarities and inconsistencies in the cardiac activity and respiratory patterns.
Although breathing period may stay at relatively fixed levels during stable conditions, such as rest or different phases of sleep, fast changes are typical in the rate of respiration rate and a substantial change in the adjacent periods may unfold within a single breathing cycle. Thus, the respiratory period may show a three-fold increase from 3 s to 9 s within a single respiratory cycle. It is generally known that several incidents that evoke naturally during non-controlled measurement, such as movement and postural change, speech, physical exercise, stress and sleep apnea, may produce significant alterations in the respiratory patterns.
The derivation of information on respiratory activity on the basis of HRV and RSA is challenged by complex nonstationarities in the rhythmic heart period patterns. These physiological sources of inconsistencies may be separated into three main categories. First, the respiratory pattern of HRV may be overshadowed by phasic accelerative and decelerative heart period responses to both physical and mental incidents, such as postural change, motor control, cognitive stimulation, and emotional arousal. These incidents are frequent, unpredictable from a physiological point of view, may have great amplitude and are often located in the frequency bandwidth of respiratory control.
Second, blood pressure control imposes continuous rhythmic changes with a highly nonstationary amplitude component at approximately 0.10 Hz. The amplitude of this component is substantially larger than that of respiratory-coupled RSA, which imposes a challenge for differentiating long respiratory intervals from nonstationary changes in the so-called 0.10 Hz rhythm.
Third, the amplitude of both the RSA and 0.10 Hz rhythms are sensitive to changes in overall physiological state. For example, when compared to resting conditions (see FIG. 5a), the RSA amplitude may show almost complete disappearance during maximal exercise (FIG. 5b) and certain clinical conditions. This effect also may be induced by the infusion of atropine, which blocks the parasympathetic control of the sinoatrial node (FIG. 5c). The localization of respiratory period during periods of decreased RSA amplitude is not possible without very efficient signal-enhancing procedures for extracting physiologically valid information from the heart period.
In addition to these different forms of naturally occurring nonstationarities inherent in the physiological signal, there are also other forms of physiological relationships and dependencies that modulate the relationship between RSA and respiratory period, or decrease the signal-to-noise-ratio in other forms. For example, it is generally known that the amplitude of the respiratory period coupled heart period oscillations is modulated by the respiratory period. Accordingly, the amplitude of the RSA increases towards lower frequencies (<0.20 Hz). Furthermore, the fact that the respiratory coupled rhythm is not often exactly sinusoidal but may be composed of several periodic components at different phases of the respiratory cycle imposes inherent difficulties to the direct use of the standard signal processing algorithms in characterizing the periodic components of HRV.
It may be concluded from the above discussion that any attempt targeted at using heart period signal to describe respiratory patterns within ambulatory, or otherwise non-controlled measurement, has to deal with the dynamics of various physiological components. In other words, dynamic changes in the respiratory period may not be successfully detected with the current state of methodology in terms of accuracy and optimal temporal resolution.
Whereas the measurement of ECG is widely available to both non-clinical and clinical purposes and can be performed relatively noninvasively (e.g., heart rate monitors), the methodology for the measurement of respiration is typically more restricted in its use. The most common type of sensor for the measurement of respiratory period is a strain belt that is placed around the chest or abdomen. This method gives information on the respiratory period but is somewhat invasive in its use and also subject to artifacts whenever movement occurs. Another commonly used method is a spirometer that is connected to a mouthpiece. The advantage of this method is in its ability to monitor also tidal volume (i.e., respiratory depth) but it is highly invasive and is restricted to laboratory use only.
Whereas there have been numerous studies documenting the relationship between the frequency of the RSA component and the respiratory period, Prior Art has not documented any direct attempts of monitoring respiratory activity on the basis of heart period measurement alone. In a closely related field, Prior Art has documented a method of deriving respiratory information on tidal volume on the basis of blood pressure signal that has been derived from the implanted blood pressure sensor (U.S. Pat. No. 5,980,463, Brockway et al.). The work of Brockway et al. is based on a sensor that is implanted within the blood vessel and is only applicable to non-human subjects. The respiratory rhythm is then derived by a simple time domain fitting of nonlinear curve to the systolic and diastolic phases of the blood pressure oscillations. It is clear to one experienced in the art that the procedure presented by Brockway et al. is not suitable for the analysis of respiratory activity on the basis of heart beat data, since a heart beat signal contains considerably more noise and does not provide continuous measurement values as is the case for the blood pressure signal. Furthermore, the described method involves an implantation and, as described by Brockway et al., may be only applied to non-human subjects.
Following publications are referred herein. These disclose generally frequency and time frequency methods for versatile analyzing of heart rate variability.
Akselrod, S., Gordon, D., Ubel, F. A., Shannon, D. C., Barger, A. C., & Cohen, R. J. (1981). Power spectral analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science, 213, 220-222 [1].
Novak, P. & Novak, V. (1993). Time/frequency mapping of the heart rate, blood pressure and respiratory signals. Medical and Biological Engineering and Computing, 31, 103-110 [2].
Pistelli, F., Bottai, M., Viegel, G., Di Pede, F., Carrozzi, L., Baldacci, S., Pedreschi, M. & Giuntini, C. 2000. Smooth reference equations for slow vital capacity and flow-volume curve indexes. American Journal of Respiratory and Critical Care Medicine, 161, 899-905 [3].
Pola, S., Macerate, A., Emdin, M., & Marchesi, C. (1996). Estimation of the power spectral density in nonstationary cardiovascular time series: assessing the role of the time-frequency representations (TFR). IEEE Transactions on Biomedical Engineering, 43, 46-59 [4].
The work of Heikkilä (U.S. Pat. No. 5,810,722) has described an embodiment of respiratory information as a part of method determining threshold values for energy metabolism. Heikkilä bases this work on a simple filtering and time domain fitting of respiratory phases similar to that presented in Brockway. It is, however, quite clear to anyone working in the field that the method described by Heikkilä is not capable of tracking nonstationary changes in respiratory period or giving accurate information on respiratory period during conditions such as exercise, since the signal-to-noise properties of the RSA signal is rather poor in the time domain. Accordingly, the procedure described in Heikkilä may be justified in the context of providing information on the energy metabolism thresholds, but it clearly lacks the sophistication required for the reliable and accurate calculation of respiratory period from the heart period signal.
Prior Art has documented related work on the general use of time-frequency analysis to describe temporal fluctuations in HRV [2],[4]. There are several available methods to perform a time-frequency decomposition of time series data. The most common and relatively robust method is a short-term fast Fourier transformation, which is basically an ordinary fast Fourier transformation with a moving window. There are also other known methods for the analysis of the temporal changes in the frequency and amplitude characteristics of the heart beat data, including the smoothed pseudo-Wigner-Ville transformation that belongs to the Cohen's class of distributions, complex demodulation, and wavelet transformations. All of these methods have been earlier applied to analyze the frequency components of the HRV. Thus, these methods also have been used previously to describe the respiratory component of the HRV, but the methods are only descriptive as such and no solution has been reported on the automatic and artifact free identification of the respiratory period.
The derivation of minute ventilation (i.e., a measure of ventilation volume per minute) is the product of two components, respiratory period and tidal volume (i.e., the depth of breath). Accordingly, minute ventilation may be derived by using the following equation,Minute ventilation (1/min)=Respiratory rate (breaths/min)*Tidal volume (1/breath)
It is known in the prior art that the amplitude of the so-called respiratory sinus arrhythmia (RSA) of the heart period is also influenced by changes in tidal volume. This effect is based on mechanical influence of the lung volume changes on the cardiac nerves. This relationship has been widely documented in the literature. However, this relationship may be only observed under highly stationary and artificial conditions, since the major determinant of the amplitude of the respiratory sinus arrhythmia is the outflow of parasympathetic nervous system, the activity of which shows large variations across different physiological states.
There have been no solutions to the estimation of tidal volume on the basis of heart beat data only. One problem in such estimation is likely to arise from individual differences such as age, gender, height, and weight, which influence a person's vital capacity and therefore, lung volume. Pistelli [4] et al. have reported an example of an equation that may be used to estimate vital capacity on the basis of individual characteristics, which may be helpful in adjusting individual differences in the deepness of the breath.
The work of Heikkilä et al. (U.S. Pat. No. 5,810,722) may be referred to, wherein heart period has been used to provide information on tidal volume. The described method is based on the association between the heart rate level and tidal volume, and therefore, it is clear that the estimate of tidal volume is purely dependent on changes in heart rate level and produces false information on circumstances wherein changes in tidal volume are not associated with heart rate changes, which is most often the case. Moreover, estimates as provided by the described method are unitless and may provide information only on changes in tidal volume as a function of increased exercise intensity. To summarize, the work described in Heikkilä et al. is clearly designed for other purposes than providing exact information on tidal volume and thus, may not be used for such purposes.
A procedure of deriving respiratory frequency, tidal volume, and minute ventilation from the R-R signal would be highly useful in several areas of ECG- and heart rate monitoring by providing information on the respiratory activity without being forced to implement an external device for the detection of respiratory rhythm and ventilation volume. Such a procedure could be applied to many areas of clinical use (e.g., cardiac patients) and physiological monitoring (e.g., exercise, fitness, and health), wherein it is of interest to monitor and derive detailed information on the physiological state and characteristics of a subject.